Maintenance optimization and reliability management of technical systems
is an important area of the application of industrial statistics. Due to
various factors such as variability in: material, manufacturing,
installation, operators and maintainers' skills, no two nominally
identical equipment show exactly the same survival or time to failure.
Moreover, in a global analysis or performance-dependability evaluation of
a manufacturing system there are other sources of uncertainty that should
be dealt with statistical and probabilistic approaches. Variables such as
duration of inspection, repair, and maintenance activities, processing
time, switch from a standard state to a substandard state, availability
of resources, and market demand are not deterministic. In this paper a
hybrid stochastic model for development of maintenance strategies, that
assure both best performance and dependability of an operating system, is
demonstrated.

A typical steel manufacturing facility contains thousands of feedback
control loops. When a feedback control system is well designed and
tuned, the manipulated control elements adequately compensate for the
effects of disturbances in order to reduce the costs of poor product
quality. In many situations, however, the application of feedback
controllers can contribute negatively to the level of process
variability. This can stem from a controller that is designed
approximately correctly but that is miss-tuned for example. When a
standard three sigma Shewhart chart on a process quality measurement
alarms, an additional controller monitoring chart can be checked to
ascertain if the associated controller is operating well, and an
investigation can be made to see if any modifications can be made to the
control scheme to solve the apparent quality problem. Feedback
controllers can be monitored via a scheme based on the control
performance index (CPI) and its associated variance. The CPI index
computes the ratio of the present process variability to the variability
that could be attained with a useful theoretical benchmark called
minimum variance control. This monitoring method is presented, and an
example of a preliminary application on a process in an integrated steel
mill is discussed.

Six Sigma is a measure of the capability of a process to deliver defect
free product. A process operating at a six sigma level of process
capability produces only 3.4 defects per million opportunities.

This presentation provides an overview of Six Sigma concepts and the role
of the Six Sigma Black Belt practitioner in leading high impact quality
improvement initiatives.

Multivariate process analysis is introduced as an advances Black Belt tool
that can be used to develop new process understanding needed to guide
breakthroughs in process and quality performance.

This talk discusses two optimal methods for assessing a
climate state. The first is the spectral approach to optimal averaging
of the historical climate data. An optimal averaging scheme minimizes the
mean square error and can be used to measure various orders of
spherical harmonic components of a climate field with finitely many
surface stations. An important formula was derived to demonstrate that the
sampling error is relatively insensitive to the exact shapes of empirical
orthogonal functions. Two examples are described: the global average of
the annual surface air temperature using 63 stations and the regional
average of the monthly tropical Pacific sea surface temperature. The
second is the adaptive gridding method for the historical climate
data. Validation of climate models requires the reconstruction of climate
fields of the past, say 1885-1930, from the scarce observed data. A field
can be reconstructed on a one-degree lat-long grid. A systematic theory
for the interpolation is described and it uses the emperical orthogonal
functions computed from the recent and more accurate observed data. The
last part of the talk will be on the opportunities
of mathematics/statistics/computing in climatic, agricultural and
environmental research.